1. Field of the Invention
This invention relates generally to magnetic resonance imaging. More particularly, the present invention pertains to methods of using the geometry of scanned objects to estimate local fields and to refine magnetic resonance images thereof.
2. Related Art
Susceptibility weighted imaging (SWI) has become a powerful clinical tool for revealing the presence of microhemorrhages, iron and calcium and, therefore, for studying aging and diseases such as, multiple sclerosis, stroke, trauma and tumors. However, prior art SWI techniques suffer from problems caused by rapid phase aliasing resulting from changes in the background magnetic field caused by the presence of air/tissue interfaces, particularly in the mid-brain and the forebrain regions. Although the presence of low spatial frequency fields can be reasonably dealt with using various high pass filter approaches, problems still exist due to rapid unwanted field variations particularly near the mastoid, frontal, ethmoid and sphenoid sinuses and to a lesser degree the maxillary sinuses. There have been some attempts to remove these problems by phase unwrapping the images and then either high pass filtering the data or doing a polynomial fit to remove the background fields. The goal in all of these phase machinations is to leave behind, preferably unaltered, the local phase information (arising from local susceptibility differences) from structures such as the veins, iron laden tissue and calcifications. However, each of these prior art methods has drawbacks. While simple high pass filtering is able to remove low spatial frequency phase variations, very strong high pass filter is usually needed to remove the rapid phase aliasing near the air/tissue interface, which results in a concomitant loss of important local phase information. On the other hand, removal of aliasing by a fitting approach requires local polynomial fits throughout the brain on a slice by slice basis and often with different regions within a given slice fitted with different order polynomials.